{
 "metadata": {
  "name": "",
  "signature": "sha256:1105da11a40c804731e937d377f2656bc48109c348d0ae7a0df22420e31a9e0a"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Goals\n",
      "\n",
      "* learn how to use matplotlib\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%pylab --no-import-all inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Populating the interactive namespace from numpy and matplotlib\n"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np\n",
      "import pandas as pd\n",
      "from pandas import DataFrame, Series"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 83
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# plot different types of graphs to show distribution of populations\n",
      "# Chap 8 of PfDA\n",
      "# http://my.safaribooksonline.com/book/programming/python/9781449323592/8dot-plotting-and-visualization/id2802076\n",
      "\n",
      "# hello world of mpl\n",
      "\n",
      "plt.plot(np.arange(10))\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 84,
       "text": [
        "[<matplotlib.lines.Line2D at 0x10e6f6e50>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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A7WUP0vcE8BBhpm+XO8qM1cCtwKOE79/p5Y4DwFsIX6Ppl5eoxs/6ZsLv3sPAzcDryh0H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\nVMoAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10dd95350>"
       ]
      }
     ],
     "prompt_number": 84
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = plt.figure()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<matplotlib.figure.Figure at 0x10c67a410>"
       ]
      }
     ],
     "prompt_number": 85
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# set up a 2x2 grid of subplots and instantiate 3 of them\n",
      "\n",
      "ax1 = fig.add_subplot(2, 2, 1)\n",
      "ax2 = fig.add_subplot(2, 2, 2)\n",
      "ax3 = fig.add_subplot(2, 2, 3)\n",
      "\n",
      "fig"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "png": 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       "prompt_number": 86,
       "text": [
        "<matplotlib.figure.Figure at 0x10c67a410>"
       ]
      }
     ],
     "prompt_number": 86
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "PfDA: \n",
      "\n",
      "<blockquote>When you issue a plotting command like <code>plt.plot([1.5, 3.5, -2, 1.6])</code>, matplotlib draws on the last figure and subplot used (creating one if necessary), thus hiding the figure and subplot creation</blockquote>"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.plot([1.5, 3.5, -2, 1.6])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 87,
       "text": [
        "[<matplotlib.lines.Line2D at 0x107323ed0>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x10e74cad0>"
       ]
      }
     ],
     "prompt_number": 87
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import math\n",
      "options = [None, 'k--', 'ro', 'g+']\n",
      "\n",
      "fig = plt.figure()\n",
      "\n",
      "# let's plot subplot 2 columns wide\n",
      "# try different options\n",
      "\n",
      "num_row = math.ceil(len(options)/2.0)\n",
      "\n",
      "for (i, option) in enumerate(options):\n",
      "    ax = fig.add_subplot(num_row,2, i+1)\n",
      "    \n",
      "    if option is not None:\n",
      "        ax.plot([1.5, 3.5, -2, 1.6], option)\n",
      "    else:\n",
      "        ax.plot([1.5, 3.5, -2, 1.6])\n",
      "\n",
      "fig.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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bZCi2JVKiskkGwLvAvwEfNfO6KtlJYAKu0qjYFmdcVGkcgtnD8RKgEljs41wi\nUaLYlthSPXWJNdVTl6RSPXUREVFSFxFJEiV1EZEEUVIXEUkQJXURkQRRUhcRSRAldRGRBFFSFxFJ\nED9JfTqwHlgN/CdwspUeibin2JbY8pPUlwHnAX2AjcBEKz0qgqUCT5FoJylthNlOAEoqtpMUD0lp\nww8/Sf0toCHz9Qqgm//uFEdB6U11ZSWTBw6kYtgwJg8cSHVlZWBtQfSDvwUlFdtJ+v/zwsIXAm8j\n6nHtZ+ejpm4DXrJ0LglAdWUlS0eP5sHNm6kAKrZtY9LmzQBcMXiw075FnGI7Rrbu3+q6C87lu1J/\nC7NfY/bjuibHTAIOA78NooNix7IZM3gwk8QbPbh5M2/NnOmoR84ptkVyGAb8F9CuhWM2AWk99Ajo\nsYlgDEOxrYfbR1Cx3axyYC3QOeyGRQKm2JbY8lOH+jOgLbA38/cPgH/13SMR9xTbIiIikizlwAbM\nVc74Zo6ZkXl9NdA3gDZSwFfAqsxjchFtzAV2Yz40a47fceRrI4X/cXTH7LG5FvgrMKqZ4/yOxUs7\nKfyNpx1maWENsA54uJnj/I4llzDi2ks7Kfx9D8OIay/tpIhHbCc9rvNqjZnU7wmcgOnkOVnHDALe\nzHx9MfBhAG2kgNcLPG+2yzHfuOaC0u84vLSRwv84TgcuyHzdAfgU+z8Tr+2k8D+eb2T+bIPp52VZ\nr9sYS7Yw4tprOyn8fQ/DiGsv7aSIR2zHNq5t1X65CBOUW4G/Ay8DN2Qdcz3wYubrFUAn4DTLbYD/\n/Sr/COxr4XW/4/DSBvgfx5eY5ADwN8xt712yjrExFi/tgP/xHMz82RaTBPdmvW5jLNnCiGuv7YC/\n72EYce2lHYhHbMc2rm0l9a6Y3dcb1Waey3dMIXfqeWkjDfTDvE15Ezi3gPP76YftOw5tj6Mn5upp\nRdbztsfSXDs2xtMK859sN+Zt8bqs14P4uYQR117bCTq2w4hriGdsN9dGJOPa1h2laY/HZf9W8/rv\nvB77MWYu7CBwLbAQOLuANrzyMw4vbI6jA/A7YDTmiiObrbG01I6N8TRg3g6fDCzFvPWtyjrG9s8l\njLj2enwYsR10XEP8Yjt2cW3rSn0nZnCNumN+o7R0TLfMczbb+B+OvZ1ZjJmf/GYBbRTTj0LH4YWt\ncZwAvAb8GhNw2WyNJV87Nn8uXwGVwPeyng/i5xJGXHttJ+jYDiOuIV6xndS49qQNsBnzNqUt+T9Q\nuoTCP7iJa52iAAAA1klEQVTw0sZpHPutdhFmjrIYPfH2gVIx4/DSho1xlAHzgCdaOMbGWLy043c8\nnTFziQDtgWrgqqxjbP1cmgojrr22YyMmehJ8XOdrJy6xneS49uxazCfEmzhWqvTOzKPRU5nXVwMX\nBtDGCMzyoxrgfcw3oVAvAbswNT92YAo62R5HvjZsjOMyzFu7Go4tuboW+2Px0o7f8XwX81a3BlgD\n/CLzvO2x5BJGXHtpx+/3MIy49tJOXGI76XEtIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIqXm/wH9\nlItLiNREfwAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10e733410>"
       ]
      }
     ],
     "prompt_number": 88
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from numpy.random import randn\n",
      "\n",
      "fig = plt.figure()\n",
      "\n",
      "ax1 = fig.add_subplot(2, 2, 1)\n",
      "ax2 = fig.add_subplot(2, 2, 2)\n",
      "ax3 = fig.add_subplot(2, 2, 3)\n",
      "\n",
      "ax1.hist(randn(100), bins=20, color='k', alpha=0.3)\n",
      "ax2.scatter(np.arange(30), np.arange(30) + 3 * randn(30))\n",
      "ax3.plot(randn(50).cumsum(), 'k--')\n",
      "\n",
      "fig.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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MCkQII7UD/kB97RxmcixC6OFrYBewE7UUYnlzwxFCCCGE0MnbqDOcHcAq8vax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\nkhmgDqxcuZKWLVsSFKTvR9S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       "text": [
        "<matplotlib.figure.Figure at 0x107320610>"
       ]
      }
     ],
     "prompt_number": 89
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#http://matplotlib.org/examples/api/barchart_demo.html\n",
      "\n",
      "import numpy as np\n",
      "import matplotlib.pyplot as plt\n",
      "\n",
      "N = 5\n",
      "menMeans = (20, 35, 30, 35, 27)\n",
      "menStd =   (2, 3, 4, 1, 2)\n",
      "\n",
      "ind = np.arange(N)  # the x locations for the groups\n",
      "width = 0.35       # the width of the bars\n",
      "\n",
      "fig, ax = plt.subplots()\n",
      "rects1 = ax.bar(ind, menMeans, width, color='r', yerr=menStd)\n",
      "\n",
      "womenMeans = (25, 32, 34, 20, 25)\n",
      "womenStd =   (3, 5, 2, 3, 3)\n",
      "rects2 = ax.bar(ind+width, womenMeans, width, color='y', yerr=womenStd)\n",
      "\n",
      "# add some\n",
      "ax.set_ylabel('Scores')\n",
      "ax.set_title('Scores by group and gender')\n",
      "ax.set_xticks(ind+width)\n",
      "ax.set_xticklabels( ('G1', 'G2', 'G3', 'G4', 'G5') )\n",
      "\n",
      "ax.legend( (rects1[0], rects2[0]), ('Men', 'Women') )\n",
      "\n",
      "def autolabel(rects):\n",
      "    # attach some text labels\n",
      "    for rect in rects:\n",
      "        height = rect.get_height()\n",
      "        ax.text(rect.get_x()+rect.get_width()/2., 1.05*height, '%d'%int(height),\n",
      "                ha='center', va='bottom')\n",
      "\n",
      "autolabel(rects1)\n",
      "autolabel(rects2)\n",
      "\n",
      "plt.show()\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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2QhFpM9H2P85C+1+Ci+XMfizwa6AjoYeGz7Xmx92rr77K448/ztChQyksLLTm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\niIiIxOD/AzDP0JQBySI6AAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x106d5c6d0>"
       ]
      }
     ],
     "prompt_number": 44
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "World"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "going back to our calculation from [Day_01_B_World_Population.ipynb](http://nbviewer.ipython.org/github/rdhyee/working-open-data-2014/blob/master/notebooks/Day_01_B_World_Population.ipynb)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# https://gist.github.com/rdhyee/8511607/raw/f16257434352916574473e63612fcea55a0c1b1c/population_of_countries.json\n",
      "# scraping of https://en.wikipedia.org/w/index.php?title=List_of_countries_by_population_(United_Nations)&oldid=590438477\n",
      "\n",
      "# read population in\n",
      "import json\n",
      "import requests\n",
      "\n",
      "pop_json_url = \"https://gist.github.com/rdhyee/8511607/raw/f16257434352916574473e63612fcea55a0c1b1c/population_of_countries.json\"\n",
      "pop_list= requests.get(pop_json_url).json()\n",
      "pop_list"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 90,
       "text": [
        "[[1, u'China', 1385566537],\n",
        " [2, u'India', 1252139596],\n",
        " [3, u'United States', 320050716],\n",
        " [4, u'Indonesia', 249865631],\n",
        " [5, u'Brazil', 200361925],\n",
        " [6, u'Pakistan', 182142594],\n",
        " [7, u'Nigeria', 173615345],\n",
        " [8, u'Bangladesh', 156594962],\n",
        " [9, u'Russia', 142833689],\n",
        " [10, u'Japan', 127143577],\n",
        " [11, u'Mexico', 122332399],\n",
        " [12, u'Philippines', 98393574],\n",
        " [13, u'Ethiopia', 94100756],\n",
        " [14, u'Vietnam', 91679733],\n",
        " [15, u'Germany', 82726626],\n",
        " [16, u'Egypt', 82056378],\n",
        " [17, u'Iran', 77447168],\n",
        " [18, u'Turkey', 74932641],\n",
        " [19, u'Congo, Democratic Republic of the', 67513677],\n",
        " [20, u'Thailand', 67010502],\n",
        " [21, u'France', 64291280],\n",
        " [22, u'United Kingdom', 63136265],\n",
        " [23, u'Italy', 60990277],\n",
        " [24, u'Myanmar', 53259018],\n",
        " [25, u'South Africa', 52776130],\n",
        " [26, u'Korea, South', 49262698],\n",
        " [27, u'Tanzania', 49253126],\n",
        " [28, u'Colombia', 48321405],\n",
        " [29, u'Spain', 46926963],\n",
        " [30, u'Ukraine', 45238805],\n",
        " [31, u'Kenya', 44353691],\n",
        " [32, u'Argentina', 41446246],\n",
        " [33, u'Algeria', 39208194],\n",
        " [34, u'Poland', 38216635],\n",
        " [35, u'Sudan', 37964306],\n",
        " [36, u'Uganda', 37578876],\n",
        " [37, u'Canada', 35181704],\n",
        " [38, u'Iraq', 33765232],\n",
        " [39, u'Morocco', 33008150],\n",
        " [40, u'Afghanistan', 30551674],\n",
        " [41, u'Venezuela', 30405207],\n",
        " [42, u'Peru', 30375603],\n",
        " [43, u'Malaysia', 29716965],\n",
        " [44, u'Uzbekistan', 28934102],\n",
        " [45, u'Saudi Arabia', 28828870],\n",
        " [46, u'Nepal', 27797457],\n",
        " [47, u'Ghana', 25904598],\n",
        " [48, u'Mozambique', 25833752],\n",
        " [49, u'Korea, North', 24895480],\n",
        " [50, u'Yemen', 24407381],\n",
        " [51, u'Australia', 23342553],\n",
        " [52, u'Taiwan', 23329772],\n",
        " [53, u'Madagascar', 22924851],\n",
        " [54, u'Cameroon', 22253959],\n",
        " [55, u'Syria', 21898061],\n",
        " [56, u'Romania', 21698585],\n",
        " [57, u'Angola', 21471618],\n",
        " [58, u'Sri Lanka', 21273228],\n",
        " [59, u\"C\\xf4te d'Ivoire\", 20316086],\n",
        " [60, u'Niger', 17831270],\n",
        " [61, u'Chile', 17619708],\n",
        " [62, u'Burkina Faso', 16934839],\n",
        " [63, u'Netherlands', 16759229],\n",
        " [64, u'Kazakhstan', 16440586],\n",
        " [65, u'Malawi', 16362567],\n",
        " [66, u'Ecuador', 15737878],\n",
        " [67, u'Guatemala', 15468203],\n",
        " [68, u'Mali', 15301650],\n",
        " [69, u'Cambodia', 15135169],\n",
        " [70, u'Zambia', 14538640],\n",
        " [71, u'Zimbabwe', 14149648],\n",
        " [72, u'Senegal', 14133280],\n",
        " [73, u'Chad', 12825314],\n",
        " [74, u'Rwanda', 11776522],\n",
        " [75, u'Guinea', 11745189],\n",
        " [76, u'South Sudan', 11296173],\n",
        " [77, u'Cuba', 11265629],\n",
        " [78, u'Greece', 11127990],\n",
        " [79, u'Belgium', 11104476],\n",
        " [80, u'Tunisia', 10996515],\n",
        " [81, u'Czech Republic', 10702197],\n",
        " [82, u'Bolivia', 10671200],\n",
        " [83, u'Portugal', 10608156],\n",
        " [84, u'Somalia', 10495583],\n",
        " [85, u'Dominican Republic', 10403761],\n",
        " [86, u'Benin', 10323474],\n",
        " [87, u'Haiti', 10317461],\n",
        " [88, u'Burundi', 10162532],\n",
        " [89, u'Hungary', 9954941],\n",
        " [90, u'Sweden', 9571105],\n",
        " [91, u'Serbia; Kosovo', 9510506],\n",
        " [92, u'Azerbaijan', 9413420],\n",
        " [93, u'Belarus', 9356678],\n",
        " [94, u'United Arab Emirates', 9346129],\n",
        " [95, u'Austria', 8495145],\n",
        " [96, u'Tajikistan', 8207834],\n",
        " [97, u'Honduras', 8097688],\n",
        " [98, u'Switzerland', 8077833],\n",
        " [99, u'Israel', 7733144],\n",
        " [100, u'Papua New Guinea', 7321262],\n",
        " [101, u'Jordan', 7273799],\n",
        " [102, u'Bulgaria', 7222943],\n",
        " [None, u'Hong Kong', 7203836],\n",
        " [103, u'Togo', 6816982],\n",
        " [104, u'Paraguay', 6802295],\n",
        " [105, u'Laos', 6769727],\n",
        " [106, u'El Salvador', 6340454],\n",
        " [107, u'Eritrea', 6333135],\n",
        " [108, u'Libya', 6201521],\n",
        " [109, u'Sierra Leone', 6092075],\n",
        " [110, u'Nicaragua', 6080478],\n",
        " [111, u'Denmark', 5619096],\n",
        " [112, u'Kyrgyzstan', 5547548],\n",
        " [113, u'Slovakia', 5450223],\n",
        " [114, u'Finland', 5426323],\n",
        " [115, u'Singapore', 5411737],\n",
        " [116, u'Turkmenistan', 5240072],\n",
        " [117, u'Norway', 5042671],\n",
        " [118, u'Costa Rica', 4872166],\n",
        " [119, u'Lebanon', 4821971],\n",
        " [120, u'Ireland', 4627173],\n",
        " [121, u'Central African Republic', 4616417],\n",
        " [122, u'New Zealand', 4505761],\n",
        " [123, u'Congo, Republic of the', 4447632],\n",
        " [124, u'Georgia', 4340895],\n",
        " [125, u'Palestine', 4326295],\n",
        " [126, u'Liberia', 4294077],\n",
        " [127, u'Croatia', 4289714],\n",
        " [128, u'Mauritania', 3889880],\n",
        " [129, u'Panama', 3864170],\n",
        " [130, u'Bosnia and Herzegovina', 3829307],\n",
        " [None, u'Puerto Rico', 3688318],\n",
        " [131, u'Oman', 3632444],\n",
        " [132, u'Moldova', 3487204],\n",
        " [133, u'Uruguay', 3407062],\n",
        " [134, u'Kuwait', 3368572],\n",
        " [135, u'Albania', 3173271],\n",
        " [136, u'Lithuania', 3016933],\n",
        " [137, u'Armenia', 2976566],\n",
        " [138, u'Mongolia', 2839073],\n",
        " [139, u'Jamaica', 2783888],\n",
        " [140, u'Namibia', 2303315],\n",
        " [141, u'Qatar', 2168673],\n",
        " [142, u'Macedonia', 2107158],\n",
        " [143, u'Lesotho', 2074465],\n",
        " [144, u'Slovenia', 2071997],\n",
        " [145, u'Latvia', 2050317],\n",
        " [146, u'Botswana', 2021144],\n",
        " [147, u'Gambia', 1849285],\n",
        " [148, u'Guinea-Bissau', 1704255],\n",
        " [149, u'Gabon', 1671711],\n",
        " [150, u'Trinidad and Tobago', 1341151],\n",
        " [151, u'Bahrain', 1332171],\n",
        " [152, u'Estonia', 1287251],\n",
        " [153, u'Swaziland', 1249514],\n",
        " [154, u'Mauritius', 1244403],\n",
        " [155, u'Cyprus', 1141166],\n",
        " [156, u'Timor-Leste', 1132879],\n",
        " [157, u'Fiji', 881065],\n",
        " [None, u'R\\xe9union', 875375],\n",
        " [158, u'Djibouti', 872932],\n",
        " [159, u'Guyana', 799613],\n",
        " [160, u'Equatorial Guinea', 757014],\n",
        " [161, u'Bhutan', 753947],\n",
        " [162, u'Comoros', 734917],\n",
        " [163, u'Montenegro', 621383],\n",
        " [None, u'Western Sahara', 567315],\n",
        " [None, u'Macau', 566375],\n",
        " [164, u'Solomon Islands', 561231],\n",
        " [165, u'Suriname', 539276],\n",
        " [166, u'Luxembourg', 530380],\n",
        " [167, u'Cape Verde', 498897],\n",
        " [None, u'Guadeloupe', 465800],\n",
        " [168, u'Malta', 429004],\n",
        " [169, u'Brunei', 417784],\n",
        " [None, u'Martinique', 403682],\n",
        " [170, u'Bahamas', 377374],\n",
        " [171, u'Maldives', 345023],\n",
        " [172, u'Belize', 331900],\n",
        " [173, u'Iceland', 329535],\n",
        " [174, u'Barbados', 284644],\n",
        " [None, u'French Polynesia', 276831],\n",
        " [None, u'New Caledonia', 256496],\n",
        " [175, u'Vanuatu', 252763],\n",
        " [None, u'French Guiana', 249227],\n",
        " [None, u'Mayotte', 222152],\n",
        " [176, u'S\\xe3o Tom\\xe9 and Pr\\xedncipe', 192993],\n",
        " [177, u'Samoa', 190372],\n",
        " [178, u'Saint Lucia', 182273],\n",
        " [None, u'Guam', 165124],\n",
        " [None, u'Guernsey; Jersey', 162018],\n",
        " [None, u'Cura\\xe7ao', 158760],\n",
        " [179, u'Saint Vincent and the Grenadines', 109373],\n",
        " [None, u'Virgin Islands, United States', 106627],\n",
        " [180, u'Grenada', 105897],\n",
        " [181, u'Tonga', 105323],\n",
        " [182, u'Micronesia, Federated States of', 103549],\n",
        " [None, u'Aruba', 102911],\n",
        " [183, u'Kiribati', 102351],\n",
        " [184, u'Seychelles', 92838],\n",
        " [185, u'Antigua and Barbuda', 89985],\n",
        " [None, u'Isle of Man', 85888],\n",
        " [186, u'Andorra', 79218],\n",
        " [187, u'Dominica', 72003],\n",
        " [None, u'Bermuda', 65341],\n",
        " [None, u'Cayman Islands', 58435],\n",
        " [None, u'Greenland', 56987],\n",
        " [None, u'American Samoa', 55165],\n",
        " [188, u'Saint Kitts and Nevis', 54191],\n",
        " [None, u'Northern Mariana Islands', 53855],\n",
        " [189, u'Marshall Islands', 52634],\n",
        " [None, u'Faroe Islands', 49469],\n",
        " [None, u'Sint Maarten', 45233],\n",
        " [190, u'Monaco', 37831],\n",
        " [191, u'Liechtenstein', 36925],\n",
        " [None, u'Turks and Caicos Islands', 33098],\n",
        " [192, u'San Marino', 31448],\n",
        " [None, u'Gibraltar', 29310],\n",
        " [None, u'Virgin Islands, British', 28341],\n",
        " [193, u'Palau', 20918],\n",
        " [None, u'Cook Islands', 20629],\n",
        " [None, u'Caribbean Netherlands', 19130],\n",
        " [None, u'Anguilla', 14300],\n",
        " [None, u'Wallis and Futuna', 13272],\n",
        " [194, u'Nauru', 10051],\n",
        " [195, u'Tuvalu', 9876],\n",
        " [None, u'Saint Pierre and Miquelon', 6043],\n",
        " [None, u'Montserrat', 5091],\n",
        " [None, u'Saint Helena, Ascension and Tristan da Cunha', 4129],\n",
        " [None, u'Falkland Islands', 3044],\n",
        " [None, u'Niue', 1344],\n",
        " [None, u'Tokelau', 1195],\n",
        " [196, u'Vatican City', 799]]"
       ]
      }
     ],
     "prompt_number": 90
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "country_num = range(len(pop_list))\n",
      "country_names = [c[1] for c in pop_list]\n",
      "country_pops = [int(c[2]) for c in pop_list]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 91
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.plot(country_pops)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 92,
       "text": [
        "[<matplotlib.lines.Line2D at 0x10e9d9850>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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MmhXuhr3iCshl+SklqY5aPsCX+vOf4bnn4He/g1tugXe/G66/Hk48EcaMaXjzklRXBvgq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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10e70a7d0>"
       ]
      }
     ],
     "prompt_number": 92
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from itertools import izip, islice\n",
      "sampled_country_tuples = list(islice(izip(country_num, country_names),0,None,10))\n",
      "sampled_i = [s[0] for s in sampled_country_tuples]\n",
      "sampled_countries = [s[1] for s in sampled_country_tuples]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 93
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# bar charts\n",
      "# can find barh: http://matplotlib.org/examples/lines_bars_and_markers/barh_demo.html\n",
      "# http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.bar\n",
      "\n",
      "plt.bar(left=map(lambda x: x, range(len(country_pops))),height=country_pops, width=2, log='x')\n",
      "#plt.xticks(range(len(country_pops)), country_names, rotation='vertical')\n",
      "plt.xticks(sampled_i, sampled_countries, rotation='vertical')\n",
      "plt.ylabel('Population')\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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F909CM/iik4SQMs4hIaujlVOeaijJiypO2oryqNjrqAxb9PrkhGPacVSoJHUV\nDq16YoTcgvSBoSpmMtKHvzrjmD2AXxEN0muj2eWPcs41DXgPWgJPRGnKF5A8ewsHT2i8CUOSBtH3\nIV1w840cHp90A4NWGXGaB+w/pRwH8F30MC8kMmRncRwyOJ5J8kN6YMK+kCVITXE+keqmCG8j8uoJ\nyfL2Aun0w/5NQQPwMhqNrEm8LnjvJIp5EH0BqVPuIHLrBc2es3gnWgG1eg/ujgbdEfS7FVFVzCX5\nt8oSYOEEoJWJzBKUVDN0JZ8ZfE7eaurtSKhMRs//K9F9ljT4DqPvshqN3lRbIfXS9innGAqO2xS5\nvV+CnpO3IfvAfgnHzAv+vhOtiL4bHLMPKk9wRMZ3uj9hXxEvu65SV+FwHbpZbyHSk4+QrG+Mcyvw\niqZ9vyF7mZn0/6TPyWIecrubjmZGnyPSpUP64BmSNIh+GOlEh5qODYXDcSmftTznXBtl/O+NwE7I\nVe9laOa1kHwvnTIMooF0L9Tf85DdIms2/000ELwR6ZffjVY3B7V47lcB/5Fz3HfRw3srjQP9oRnH\n/Bap1oqsMOKUuQdPQAPb99A98R40IOYFl/6YRkG5LXrOslR6l6BB+nKi2JURst22Q1fW8FwT0Pfc\nMqd/i4K+XBmcEyTcm20lcS5Ek5TQMLwFej7yvLcWAv+O1GsgFdlP0DOQxi2MttEk7YsT96ibggzt\n65BvozQJlPXEuALNpkJ2R7OVLJK8Bop4VUwKPv9HqL9HohnFu9AgUVcmIbXSp9BAnbW0PyphOzL2\ntygboxn5cznvK+PtlcZtOf+/k9YnVz8lWwefRpl7cCmNDgetegOFzCJf/XdAwrZ/zjEnAr8I3nsg\nchYp4o4eeqTFPebyPHuS1LpFVL3LaFyBTiFflXUnMkKHvJRi6tpmirr1do26eiuV9cT4CJpJhRXl\nHkLqmSxuQSqar6LB4D/Id1kECYBhdMPHPSN+QHqgz3rA/yK9dFj0aHuSYx2y0phnzdo2QyqhV6X8\nP+um/CWKbbgODbqvJjvCfE2SVylJqrMkBolWD88R+Y2nUcbbCxpVcxPQtXk455jbkNfVIwU+P96/\nW9F1DFcPeTNsKHcPjiD10x+D9tqUy3z8EPk5zs4s8bmfRCrbHVG/vkl6bEmc21HcwSQ0aTiMfM+j\nJShGJlT1vJdiNpuzkRrpwuC4d6DYmyw+jlY1oapoEBmbs4jbRiag52pi+tt7Q13VSk8ho9Q/iFzl\n8qI446xmU82uAAAgAElEQVQR+5wi7/0MkeH2cuB48lM/rEm0HC3Kz5AB+3/QknsVNENKWjIfQKOd\nIs4I6Tfxt5G9ZpjkwSLLsPh/6Mb9O3ogr0KC4umMY8pyA/IY+j6yO+TFiIC8vU5FaoevBvu+Tf7y\nfIjoWjyLVG8/JNsAPozUOjfSONBnGR4PSNiX9VuFlLkH90GqpeGgPRcZms/LOVd80jEBfcf7Sdaz\nh/RSZz4VPR9h4NvPkZo267daDUVqh+qgq5HrbREHh22C40LHgcXZbwdad1QYZvT9dxLFYjG6Rl2F\nQ6vEjbdJ+vk04207lDGa34wG38VE+tRW7Ru9YE000H0CzcwnZ75by+xT0CpoBAmWj5M94IcrnLJ0\n29trXsr+4S6drwwvJnK2uBF56uVxAKMHqmtyjmlFZ56UniOklQkeaHa9BsVSaJRlIrrH404HeV5s\nrToqVJK6qZU2R/q7VlUioU90mpoji03RIDhI4yCf53N/MZppXE6j0TyLp9BDFfJa8m/8MM/UHKIA\ntSL9A93EgzTeB1k38aFoFrUNmi2egYx2eZyLVHl7BO29kefWdhnHPIpWKvHcSp8l+XrEvb2SyNOZ\nX8poV9snUfDUN0meYQ7nfGacC5BxPEnnP0K6h86XgcNJ9jLKW6UMIGEcqm0mUkxtcyYS9psExxWZ\nvTa7dp+CnsUk4RCu2o9HKrnvBu19kTDLo0y+o7Irm0ORIftxGu1dWUbzNEeFpOeqH5PWwtRt5dCO\nSgSSdd2TyV72LUFL0EVEP/YI+TrfMjP+bdCy/uVItzoTzcKy9KOXI7XLJ9BDcwCKPcjTz5fxtvlP\nIi+xvGjlOEsYPQDmeYldiAbTs9Dv9r7gM/ZIeO+Z6DdZFwm8XwX734BWKW/L6d9X0Ox3AVHk/JNI\nqE8j2S61fXDc5ugemoiEe9LM98VoINyQ5GdueUq/tkHXel7K/4dT9oPu2dlE32kvtFL7WMYxBOc6\niyg76gbIuHxV2gEk68w/Svbvm3RPJO1rJrxv9kWTxP9Cz2bWgF3WG+he5K31x5z3xbkTTdSKTELL\nehyaLnAVja6a25Lv6VDE+JzE8cho3iqrIBtD0UjisnmmynjbgAaCw5EQSVvBhcxAD+EXkAvlYLAd\nTX7UbZJAzDMiXo6MxCEvQh4xeSRdr3BfUt4g0H2xMVIBTkQeN3nf6ShkKO8Fd9FY6XECxdR0i4gC\nD0EriDzPmWGiaPTL0SRu06wDkK1qP3TtJqLBvkhKizL5jpIo4g10JcWewTgXUGwFVHnqplaK06pK\nBJQm4qdodr4+yuB5QM4xlyLvkOZ8PVnBYqCgl0/RmtF8fxrVG+Hgm/W9yuaZKuNtcwxSj4TeG/OJ\nssImsYjGGVHotRHOjLIicZ9GKqxQbbUj8kLKYhaNevXHSI9+jjMVzerD2fKGwT6Irm8Sd6OB7Tl0\nLW4l+zutiYTVCmQYviDoYx6boHu3OS17llrkHvTdlwftDWiMrUljEo2qpN+SP07MK/C5zbwXqYPC\nGJlrgn15lMl3VNYb6H4kIC4jug/y1D0zkZtsEUeFY1M+I+xrXvBmV6mbWimkjEok5A1odvMEMvrm\nGemWk7xEzAoWK8tpsXNNQd4pi8jOO/825FY6Cwm9aWiZeknK+0P99Rro+7fibfNbtOwPdfCrodn8\nJhnHlOUVSCiuFbRXIOGZtXo4LehLPLHi3eTfF/+OUpiHBvKXIvXLlUiNmRTkdzVKkvYdlFDw0aB/\nRfL2bE2USvsh8vP2X4MGkpNRNPWBaHBLUouEv+80tDK+Ef2u2yIdfZobdch89EyFbp/7ogE1y5Fi\n7aB/RexD7TABTU7Oj+0bQNciS805TDlvoKHgb3P2gix1z7yMPjTzCUaPLVNR8OULiCYofaGuwqEV\nvV6cz6AB42A0yIUBWXnFaspQpphOM2ujByEtodtEpOJpxXB1MMontZDG338nNMhl1Uy4Eun8VwTt\n6cjlM8/4Ha6IminiwbEWkYG4CHvQ6LJYxAgLje6HRfI4DaJZ/6rI82oaSpBXZHb+IiQY9kFCOk/P\nvgitIpcS6dbDfc3My/icMI1GFlPQSnmHoL0Qfa8su1wr9qGQsEhVc6qTIilw8rLE1pFpKGbjIOS+\n/SW6V6VyTFNWr3cKjSmnN0SriDy2QDO998e2PMoU02lmVfKjqW/K+X8zl5E8GG1Feu6dU4PtR0gN\ndWawPUyxwfe02Gd8B83Q84rVHIEemAEUBLiI7Kynk2jP9XVLNHHYn+K/cat8DM0g70CzzzkFj7uW\nyNvoEDTodtMHfjJa3WxFfnZaKGcfKluk6gQ0456Fnqlwy+IF6N5bjO6jL9PoFZjGumiF8RMim8qv\nMo+Qo8JNyDnhn8ipIWtisw6yT96P7okiKmGTwKXBdiXyX/9FbF+aGqWZ1cg3lsUZovUqXFCumM6l\nse0ydMN8IeeY/0OD705oJrkN2YbiLAGVljLiAPQA70/raRKSWJvk0pFxQiPjW9CguAX5AUgXI4Hf\nKkMU/42XZmx5htETKBezsi2yV8xCQvlCosy4abQ6SIW8FdW3CFORP4jUbllcT2O+oR3JL+ZUtkjV\ncvRcNG9ZXIG0BhshleGnKVYJrkzFyVYcFU5CHlFHUy6tiolxMPph5xKVa5yHfvgPph0U4+1oxrU8\naL+SfKFStsTgDcFx4YA2k/zBLf69diS/oAu0XsMgS+1RRCXSCYqsiMKYgK8QqSfyrt9CNBj+itYm\nDa38xoM5Wx6vQDaQQyhfV6AIZbypQM/Hy2Lt2eSvUl6BBvkHgu1W8r9bmUqJZUma9BTJM1XGEzD0\nbowfk5YLrrneRnwrqkY1AWVUInEWoVlrfJDJS7BWtsRgmWI6xzBaIOTlZUnyWMnyYjkv5TMPptHQ\nl8QmaEZ9B9GMrcgDXWZFdCZaGd6DDHPTyHcrnhdsc4kEbZ4BFsr/xoMo7TQo0DJv9nc4ut8+i1Qq\nS8nPqwTKrnoRum+LrlJaGaTiNKspBxL2pTGN4hHOZYtUTUWTwW8H7Y3Jj2M5Gdl3JgTb3kinn8f1\nwd9fBOd4Ffnp469Garlz0CrjSLpXe8PEKKMSiVMmo2OrVbjig/vmaIZ4SPA67yZ+HPlxxw28ebPl\nJH/trEF0PbTkv4qoIt5V6EF4UcZxIK+ZN6NrtiFSx+RVqoPGQXsHiq2IJqKHce2gvQ7FZtplqouV\nqbT2ITRohoPFJuRn+F1KowfKVIrNYH+LVr0vpfgqpdVBas9g+zrSsR8QbJcF+7Ioq9Mvw/eRGiaM\nP5lK+vd6imgm/jzyVHo2eF0k71mZipODSHW9Fno+TqZxJWa6RLsqkTOQB9FSNOM4FRmO0xig0U9+\nI/IHqGUku7l+gPxZ9mI06N5IFOGcJhw2Rw/zfUjtsmfw9wDSA7dCBpAAOgypOIqk2oBIEC1N2JfH\ni1AK890olimV4P1fCra8gjigldkDyAvqbKQ+fHfOMWV+Y9CANJnG36dIGu24Q8RqBY6B/NxGSQwG\nnz8NuZl+iexB6kwkEOenvM6ijE6/rEAJJz5FbXkTKBbrYpqoWxDczWjG9q2m/QdTLJL5UJTR8RmU\nViDM6JjFT4iyouYZvkBujb9Ahr1Qr/7fSCi9Pu2gGA8E7/sGUuGslvK+TdCAuRaNA+dKdD2yGEF6\n+TzPi2b+jmb096DV0CMU88X+IFKZhbaQ05BqJSkVeUhzsZrDUOBjVrGaTwfHNFcXyys92epvDLqH\n4u6d8SRracxHq9d4CugzCpzrOHStrqAxGCspZ9Q7gJcQpaW/Cl0H0OowbRJ1QIF+pLEejc/R8Uh1\nk8V5Qd/2IEqjfT6Rmi6NZ2h8JmaT7Wb7PI2/bxHKpMPPEvJZ+bNMh2hHJVKWs5C3SCu8CakbtkDu\ns9dSzEWtOcbgP8hfbaSVOuwGZbxmQEIyPitch2IG6VaL1SylMXZjQoFjoNxvfCKaaCxDwXAXoVoc\neYTpRw4jyrybx/fQxOgsohl92mz+Whpnyrei670B2ZOBo4O/pyZseS6mZXT6ZY3EO6Nn/gkU7PgA\n+TnVWv19DyDyzGv20kvzzhvM2WpHHYPgBtDNsAWSyLeTPwNuzroZJy8qOPTeeIDGEoh5M4HXowHj\nGqTuKJI7HooXTw85Ec3ankb1ILZGq5dzCh5fhtDoWNSj4lr0m4UzvMloFfG6jGOWBMeESc/WCY7J\nuu4nou8fj5BeQn4SwjK/8QS0IorXFfgO2auH0B8/nv11JVF6laz+hQF6eYRp30NOQ6s80KolLRPu\nbug5OSDhfyMk15yIp9+eSpR9eAK6jlkG+pORzSZ0gng3GsCPSj0i4gVEk5LrGZ0Vtpmyz3CcounB\np6Jn/TnkMr8pStmT9xtXjjoKhzI8gdIULCAySscf0KSo0Q1Q3vZBkgXL8pRzxR+YKUgNEE/ZneXN\n0Urx9JAwS+U7kRH2SOQe2I1l7GuQGiT8Dn9GEZ157n3nIGF+cdDeHQ3aS0jPVRMWq7kSXfu5FCtW\nE68u9muKBekNpuxfnrJ/Epr5blbgs5s/bwMaI8wfDbYs1eh85BOfZ0sCrVhnp/zvPvpctJ7yAiWe\nHwkan99n0LOaZmQeTNm/PKev5yInhVbSgy9C9990NDG8CY0B++acq3LUzeZQlhehpf8+wXYZEhRZ\nD9vFaHBejlJE7FnwXGvkvyWVITSzC3Xzi8l/mMPf8G3IRvEXypWDLMIZKMo3ngzvDPIF0b3BFvbr\n4uB10rXaGgm8BUhov4YoSd87Uj4/rYDMh9DAcQ/ZRtLlwd91aUzlkMazaDa6IVGyviJcjn6jMABw\nZ5RGYz7yCEpTfWyP1EP305gHK+m630CyXe4jZAeZ5dWO+CNKend9wv8J+jJI45iSZBNZAwmCl5Bf\nNCfOl0i/ryeh3+KrJLtILw/+Fv19Q16OVsf7otl/mB48SzgMoASRByEvuC9iV9baMBktnf9AtNxO\nYnHK625SxtX2BBTBeSsKLluXYpGmZUi6Dq0UQi9ivL6PRrVIyHEp589jEhI4WROBtyMX1r+iAfj5\nnPdDuYC7LD17VgzCYMqWxAuRXW6YyC43jAb1LC+xMF/RvJRtT5TTLIn5FLeJhBRxPW+FySj+Joky\nvy+USw++GAnz65FwgWK2FNNHpqAb/AK01PsM2bn1+yEcWnW1DZlBZLydSnFX0aJsE2ynoNnjvGD7\nOkrfkcfr0IP7YNB+BZpVpZ3rPiJ7xAR0DYZprYRkM1kBVkuQHjv8nd9AvhfRvJQti8uR4XdDNLh/\nEq1mJlJMyK6L1FLhlsYAcopo1VU5ZCaRh1OcNPXmHbSuoi7jBJBH0qQCyv2+oOv3MFo1TEC/WV7l\nw7lokhAa+GdTLGeU6RPnoIfveLIrRsV5jiiA5ll6E9o+FUVT3xxs/0v+MjieKK5bSeOGidJyJL3O\n40Y0mMWFbNbMbSukhtoFqfR+SH6d6nYIdf2/IRKyZQrI5DETGYgXB9tpwb5VyY5BKDvzbYUBpNb8\nA7KJrAheH1vg2LOIZslFWYaesfsoHvVdlk79vgOMH1X8uDFIP0/kpdBMnpG46pSpAdFrbkSzxMVE\n7ptpZUJDj56XoyywlyP1X2i4zCuyVIYrkEH/82iG+TiahSZ5U6XZN6D4vTSV9PsxiSVo9n85un5v\nQGmx89Jbt8KRqPjVh4hiPV6KVm0/Izst/Dw0W36UfJtIyGDK/uVFOtsirfy+0OgxFf7WcQN40rXI\nSt+T5xFZScaLcKgLm6J0xINEM5QRWlML5NWAaIfpaFUySGP/8vID/YAoe+x2wftfDbwn4b3LGV1c\nJWSE7njbhO6HYQ2OaSi2oJXawUV4HXJ3DWNFtkZ1hPPqOoc1DH6DUoo8R7F6y61wK3LaeKJp/0wk\nlLKyyd6L3KdvIxLiUGygbzYSt2KkLsoayNW76O87RLZTR1Kxn3kZ70/ziDSmMEtQYfbt0OD5alov\nbFIk42lZrkOzpgNpDBLKYyZyC3wcDT7fo3u5d8qwEaPTWgx24TytqtdCrkAC5TTkyvsVitVbboUs\nA3Ge8TgvPXcSZVVlzfa/u4Ity7kkzkwaa2sbUwuKpABppkzG07K04pmUxRpEBrsqcAuNRW0m03ph\npiKEaapbqfEBWtlMQKu1/dHKq9PCNcvpIs8h42tI+O9DlMAvqwoclDcSJ/EClK6mme2RXexCtOK6\nDam+HkcqNJPBuDGu1IRLUcqMC2nMF5OlZ4/7fz+HBpEkdU0nOBfppC+lWP9ejHIhzSZKVX0w0ukm\n+cD3i4lEOYtA322VLpznd0TlN1dFg3yaeyhk2zeOIT9+oxW2Ij2ILC2/V8jq6Prt3LQ/6zf+JzJ4\nT0DX/0oUa5HHoai+9YrYvj+giVEzp6H7by3kcrwLcjHdDK3AflrgfMZUguW0XuUKNCs6EQVkDaMH\nqBscgoLsHqBYPYcrkP52F+QGuxw9lJ12tW2XK1DUdsju5KffLkMn1WtF4jeqTFlV2f8iofh9dF9l\n2U3jsSPNQrhb7ulJGZlf06VzGZPIpmjgvRPl7j+U7hjz4tyPlvBFaQ7seojGZHpV4WUocPDBYLuO\n7uTgT6pj0a6gLFIgp9tsioRpKKi2QiuaLNZA98IqyHbViqpsAhIM5yFB8f9IThmSFauUJRyOytiO\nzOnbIhT9HTKXzgf8mXFIK1Wunkfug/FgqKLppsvyC4pFOYcsISoAv05TO68ofLOa4ArkUplXMKkd\n1qC99Cd5PIsGtNVj+3oVYNlNrkZOFOF3GaC1FU0ZI/ErkBpqGQrGXIxWz3GyYpWezfjsIRTf0byF\n+7N4DbJXrYdqb/+GYsWtKodtDtViPjKOhv7XjyA30B8nvHcPZAC8Gg2aF9B91+S/odXAlTT6s6e5\nsiaV9gzbeW6pzTUp9kc5stKyipbhfShA8ihGJ3VL82dvh6UowvYalIW0VzW7u83qNKZsGSE9C+n2\nKN7gTygo9Wy0Gp2AfuM8O8DhyJ36j8gt+BPBuSYg76f/jL237Cp1qORxoOwLhyH336eRe/DjmUdU\nFAuHajEbpfcODcpZgVI/CrY1kI7842gG9nWUifQXXehfeM44Wf7gg22c65Gm9sPB1kkvonAGvybJ\nwqEbfBUJ2EvJTyVeF56gUQ33LuD3Ke9t10g8A02MmhMePk+xaoFFKFPspzkIbnWUtfh0HARnOsC1\nqBLWNSgSdjbKTlo0B80M9GC+h9bz6VSNTZAu+eVEQVLdCoLrFfEI8Reh1d425HsDVZ3ZKAvs9mhA\nvB8Fmy1PeO+tRAF1d6JytyHx65PFTkgYzUcTojXorEr1AEZHRoek1baYl/C+8PhaBsFZOFSD0E98\ndVRdbA5alu6AbtQi+Yu6yQVIDZKUXbJbJRCvQfrdk9GM8ECkJvhMB8+Rpj8OH+zPdvBcYYW0BbF9\nqyAVYu0GjiamoAjzMB33k0hVlFSEJy4AmoVBEeEwhATqpmgCsT7yXNoh45hesx5RqvkbqalaycKh\nGhyBBo4XI3XQg8jroUiVq17wXiS8BhP+txfZ+e3Lsgi56C4lSpYY7usUn2C0+mgqysX/Alozvhch\nTIMx1liK4l/CSOk9USr5jRPe+xyyXYFWTE/H/rca+aru3yABcguRIOl0KpGQdZHqbw7R6i4vnc1e\nyDAeCvzXIztIXh1zYzIZRAVFbkVeGMei2VG/eQ6tXl6S8L+i3jYTkfArknIapGKbiOwnhyA9c9HS\nqWWYhtwvwwjzdbtwjhOQQJpFca+tOrAlMsSeiCYRPyf5XukEzVHmU0nPsPoUjR5KrWZWvhyVgr0L\nuaTOJ38itITGe2dmRv+MKcUrkZB4rt8dQQ/ih9CK5t0J/8vjULQCuoMoPXNeAZRtiRLUnYmibV+b\ndUBJ1kFeM/ejhGrTu3COkOWUC3KsA+9Eg/Hv6U6MSMh/opoi96N78nryEz8ej5IbTgu2j6K663mE\n6WLig3ueQ8RSGjUyE3CxH9MBJiGvhnOBx5D3xu6ZR/SGUABsgh6O+UQqlyLC4V6qlWgv5CTUt6NJ\nr11s8jkdqVE2QtmAW0mEV4ad0W93EnIVzSNp5l5kNh+WRP0Fiq95FbpfsjgxeP8ByE72M7qjdjXj\nhJ1R0rHHkEvce+luMFarxAXAKkg9sozGwKcsrqR4rqIwv86lCVteGc5WeR4ZUsuqHVqllSDHOvFx\nGmfLayGB0Q2mEsUvbIomU3n31nXAfsFxE5EnVZFUHbuhFPhborQ0i8h3SR1ANpewROs7C5ynktgg\nXQ1+hbxYfkh3itm0S5IXyTwaXQmTCIumzEF+7D8mSnCXFmS2DTI2zkv4Xy1dAmN8H3239yMX3alo\nkEoqemSSWQTsiNR/1yBbxz/QgJ/GRmjSEQaXXoOC6ZZ3qY+htxIoOLCW3koOgqsGVY9JSHLpHEbL\n7KzcPmFw2e+QvWJVGlNjJxFGUA+j1NmboRn+Mhozp9aRVoIc60SS3aRbMSkDyNvpIOQC/kXy057f\nT7kgtHVRpP4gjcWtsirwNXsrnUpNvZUsHEwRLkrZvwKlQkhjqI1zvhWVqAyzvr4UVU37SRuf2W+e\noTHgbTaNqc/rSjzr6BQUiNlNG9P2aKVwUNDOy8tUZpAHuBilp7mcqMJdXuT8p9H1CFcLM1FSwtoJ\nB2N6weVIdxsyA7k7ZrGMRq+X2XTXlbUX7IxmlE8gp4MHUJGbsUinCkM1MxfZnsJiUbNRuu8srkPu\nyXshwfUuZBfIozmrcBHsrWRMCyQ9ZHkP3k1N7YGEfXXkBcgI/TZaS39eZbZBKsZXodK2H6FYhbte\nUWaQB7nAJlWYy2LMeCvZIG16wS00JksbJCrd2Ew4o3szsCEy4oLiK36HfNTrxjaMTuxHbF+3Ztm9\nYpjouzyLDL0n0Z2V3qYokHCQRhVRlt3ueLR6SKoWl0S8At9UZOsKs8yOoFiJLPYkSuexkHS1bKWx\ncDC9YBeUmO3qoP16FMD0s4T3nsnopGXx1wd2rZfdY5joe7ya0YFUY1G1dASq/tdplqDMw4toDBDN\nCk57iqiUaSuDfCtsDLwQ+HXT/h1RYGBefIQx45aZFFenTCK/4lZdGQvFfYrwYJc+t7k+SDfZgchN\n+33I9XrDlPdeRnJ+p60Ync7bGBMwAT1cxwTtDchPQz4W7AtJWDi0xxDwHyjleSv5qaaje+71sS2P\npeje3Rr9boeQHmeTtXJxmVBjUvgG8kkPi7zPID9Hzf+hwjA7IdtEaPSsOxYO7bGc0bmp7ss6ALmx\nLkW1Jq5EmWB/VeBc4W91LErAB+n2oayqfrWs+Oc4B9MLtkMR1uHD9ifyUx68EumFmwPw6qifj1cW\nWx+5XsaN0nmJ46pK3HDbzOop+9thAnJhPb/F4w5HsQfXoftnM7Ljc0JWAp9CqTd2Qqk30u7bm5Ed\n7VtN+w+mt6qwjmHhYHrBP2is5zuTKKgojXld603vuYVoEI2/7mY50l7Q6/xfz6P6Cq0Kh78T1Y2Y\nghIDblrguL1QsN0HgEeROvTElPcegbyS9iUSBtugKP/a5lcyptvshwKXHkalP3+LHrws1kPJ20KP\npjlEEbFm/FKmJsZFyOYwhFxLLyE/0n4SrVdgHEAutYehNPVVT4tjTCXYHBn0DqGxbnAaP0PV8cLU\nyqtgw55pvybGPJRnKS/HFyjtxdq57zLGlOYMRmd1Hco5JjRYxw24ZSNdjSnDJciwfjqyG51KfqqO\nMUNewipjOsFbgLOA/WP78ooYPUVj8rbXAn/pcL9M/ehlTYwLg3MtRHaEcBsX2CBtesHjaDn/PeS5\ndHiBY45CwUMvRTUPZqKEaXUk7q00QmNmgjp7K/WD+WiADmszPAL8ANUK6TRnduEza4NXDqZX/AVV\n1noCGfrWynn/LSgD5w7IRXAO1Urm1grhjHMyitX4LXA38AqK6b5NxGyUYTWs7dHNmhibIMFzB8Vj\nKowxLXBcU3s30oOQtkXRryH7I93vVygWCVtlbqDRT36VYJ8pzrWoJkZoi5oN3JhzzPYo4v6vKLfS\n8xQrA3sNSgC5BKXNGAI+13KPjTGj2BglHmtmRxprNcRZTCQEXo+Slu2JMmv+oNMd7DHLaLSjzKD+\nNSp6TZmaGLege3Exirc5ELnE5hFGQy9N2GeMaYMyycjiqqOv0ujVVFe1UsiBaDA7K9iWo7z/pjVa\nrYkRGpGXxPYV8Xy7FgmTi5AL9h6MI2Fug7TpJi+k8YEMWYKKvicRpij4J1rSfyj2v7rfr/NR/MZ2\nyBB9NIq8NcWZi67dyqA9J/h7dfLbAamTJqPJxRfRNS9SruAIlAbkMKROmkajx92YxvUcTDe5h3T1\nUdr//gdV3/oDioLdBumIN0beIzskHFMn1icqVBOmzsga2EwjPya6blOQjeoWsqORB4HHkPH/42iQ\n/xo1TYjXKywcTDc5Dxmek5KRvRlFQCexPUqf8Qsib5RNUC6fOut8v4C+8x00FqrZrT/dGRPMAr6M\nVD6d4lJGuxyHjKAI6zGPhYPpJushfe0/SE5G9vs+9atf/BbYEnim3x0ZQwwgYZuVkiUpvcYIiqFJ\n4gngIWABkTdZPItuWk2HMUXddbim2jyKgpXeAGyBHqwfUyyX/ljkXqTasHAoTzygcAKKFcmLWn5N\n7PUUFEy5Tsp7Qa7U/wbsE2yXIUFxe6udrTNeORjTOy5EVcV+SSQgHCHdGgcQqXyeRR5fzXWbi7CI\nYsWjJiMBcRLynDutxLlqiVcOxvSOS4ItTp3rOfSDM0scsw3RdZ4AvJrG+iJJTEGOEe9BBu0vIxXp\nuMErB2NMHXgH8BKimfuNKN8WqADQBRnHDhMJh3C1cRLpMQvnAC9HNR/OpzEIzhhjOo5z9ZTnWlSJ\nLeRWZDfYgM7bsJ5HcRRJW5G0G2MCq5WM6R3zUbH6k1GW2gPJV28YsSrwu1j718Afg21qzrFHMVp9\n9xdkyE6KlHZCUmNMT3GunvLcm/G/vNXXuciN+EtIMC9DK7ibUJS6Mcb0lXGdq6dNzqUxlUrIR5Cb\naW3+a7IAAAOLSURBVBYLUQBlyBooKn114M6O9M4YY9pgW2BNFNV7JnJtfW0/O1QjXghch4zLJwfb\nMHA9CrbM4i4a62ZMJhLKi0e/3YBtDsb0krDuwEqcjbVVHkMBlW9EnkStBFR+D0U6/wh5aO6GViJT\nkXOAScCurMaY8cBrUNLGEVTE5+b+dqf6WDgYY8Yq05DraVg8Kp4fCeBPPe9RjbBwMMaMVS5DUc7L\nSY5ET6spYrBwMKaXrAYchHTmU4J9I8AH+tYjY1JwsIcxveMc5HXzFuRp8xLgqX52aJzwy4L7jDGm\nL4TRuGHp1FWI6gWYzrMaSrGxBNkdwm0QubeaDOzKakzv+Efw9y+o6M+jRMnjTOf5MHA48GIaaz6s\nZByl3jbGVJ+D0cx1Lkq69wSK8DXdxfUySmCDtDFmPPA6pE6Ka0vO7k9X6oHVSsZ0n/chY/RRsX1h\nNbMRlArCdI/vonrRtwLPxfZbOGRg4WBM91k9+Lsmjf72A7gSXC/YBpiDr7UxxpgYFyCjtGkBrxyM\n6R3rIqP0INGz5yC47jMTJdi7EXgm2DcCvL1vPaoBFg7G9I6LUR2By1EpSrCqoxcMJezzdTfGVIak\nkpSm9+wEfK3fnag6rl9rTO94CYravbvfHRmHvAo4EvgO8Eq0grsx8whjjOkRTyF10t9RlO5KlFLa\ndIdNkUrpTiQMDgV+188OGWOM6T/PA5cAG8T23d+nvtQOZ2U1pnfsQFTo/n0o+G3D/nVnzLMH8DRa\nNXwDeBPOCmGMqSBL0YRsa1TY/hDgqr72aHywBrAvqjn9V+DrwM597ZExxsRYHPw9Fvhg8HpRn/oy\nXpkBfAj4Vb87YowxIVcDn0LeSushb8Glfe2RMcaYvvMilHxvp6C9AfD+/nXHmHRsnDGmt6wHvAZF\n6N4IPN7f7hiTjIPgjOkdewHfB6YCLweOBx5GeX+MMcaMU5ag5HshM4nqSRtTKRznYEzvGEClQUP+\niFW7pqI4K6sxveNnwM+Bc5FQ2Bv4aV97ZEwKnrUY0zsGUNTujsggvRC4qK89MsYYUylm4smZMcaM\na7YHhoELUbro24BHkRvrrv3rljHGmH5yC8rl827gz8Brg/2b4QJAxhgzbokLgDub/rcYYyqIXVmN\n6T7xesV/71svjGkBG8SM6T7PAX8LXq+GagwQa9ul3BhjjDHGGGOMMcYYY4wxxhhjjDHGGGOMMcYY\nY8T/B30xO2aalmpTAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10eba2e50>"
       ]
      }
     ],
     "prompt_number": 99
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# what if we make a DataFrame from pop_list\n",
      "df = DataFrame(pop_list)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 95
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.plot(df[2])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 98,
       "text": [
        "[<matplotlib.lines.Line2D at 0x10ef14b10>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x10e790d50>"
       ]
      }
     ],
     "prompt_number": 98
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.plot(df[2].cumsum())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 100,
       "text": [
        "[<matplotlib.lines.Line2D at 0x10ea9e110>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x10e92e6d0>"
       ]
      }
     ],
     "prompt_number": 100
    }
   ],
   "metadata": {}
  }
 ]
}